Active battery management method

ABSTRACT

Method for active battery management to optimize battery performance. The method includes providing signal injections for charging and discharging of a battery. The signal injections include various charging and discharging profiles, rates, and endpoints. Response signals corresponding with the signal injections are received, and a utility of those signals is measured. Based upon the utility of the response signals, data relating to charging and discharging of the battery is modified to optimize battery performance.

BACKGROUND

Battery management is of critical importance to a number of commercialapplications ranging from consumer electronics to automotiveelectrification and grid-level energy storage. Battery management isalso a key component of the value proposition of any battery systemwhose utility depends on delivering a minimum amount of energy reliablyand safely over an extended period of time, from a couple of years forconsumer electronics to a decade for grid installations.

SUMMARY

A first method for active battery management includes injectingrandomized controlled signals in charging or discharging of a batteryand ensuring the signal injections occur within normal operationalranges and constraints. The method also includes monitoring performanceof the battery in response to the controlled signals, computingconfidence intervals about the causal relationships between the batteryperformance and the controlled signals, and selecting optimal signalsfor the charging or discharging of the battery based on the computedconfidence intervals.

A second method for active battery management includes providing signalinjections for charging or discharging of a battery and receivingresponse signals corresponding with the signal injections. The methodalso includes measuring a utility of the response signals, accessingdata relating to charging or discharging of the battery, and modifyingthe data based upon the utility of the response signals.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are incorporated in and constitute a part ofthis specification and, together with the description, explain theadvantages and principles of the invention. In the drawings,

FIG. 1 is a diagram illustrating a system for implementing an activebattery management method;

FIG. 2 is a flow chart of a search space method for the system;

FIG. 3 is a flow chart of a signal injection method for the system;

FIG. 4 is a flow chart of a continuous learning method for the system;

FIG. 5 is a flow chart of a memory management method for the system;

FIGS. 6A-6D illustrate the search space of all possible charge profilesfor the Examples;

FIGS. 7A-7C illustrate that the algorithm in the Examples identifieddistinct effect sizes for old cells versus new cells; and

FIGS. 8A-8C illustrate voltage versus capacity for a charge profileassigned by the algorithm in the Examples.

DETAILED DESCRIPTION

Embodiments of the invention include a method for improving batterymanagement by implementing random experiments on the charge anddischarge variables and inferring their causal effects on utilitymetrics such as energy capacity, power, fade rate, charge time, internalresistance, state of health, cell imbalance, temperature, cell swelling,electricity cost, and more. Linear combinations of any of the aboveutility metrics may also be defined to give the figure of merit whichbalances competing requirements. This battery management can be used in,for example, electric or hybrid vehicles, electric bicycles, consumerelectronic devices, grid storage systems and other vehicles and devicesusing batteries.

FIG. 1 is a diagram illustrating a system for implementing an activebattery management method. The system includes a processor 10electrically coupled with a power source 12, a load 20, and a datastorage 22. Power source 12 provides power for charging one or morebatteries 14, 16, and 18, and the batteries provide power to load 20.Data storage 22, such as an electronic memory, stores profiles andparameters 24, external data 26, and results 28. Results can include,for example, time series of current and voltage, energy capacity,temperature and more for each cell or string of cells.

In use, processor 10 injects signals to power source 12 using profilesand parameters 24 and possibly external data 26 in order to evaluate theperformance of batteries 14, 16, and 18, for example how the batteriesperform for charging and discharging of them. Performance metrics caninclude, for example, delivered power, energy capacity, and fade rate.Processor 10 stores as results 28 the response to the signal injections,and those responses can be used to optimize performance of thebatteries. The processing for the battery management can occur locallywith dedicated firmware on the battery charging system, on standalonePC, or be cloud based and occur remotely from the batteries.

Profiles and parameters 24 includes possible charging and dischargingrates, charging and discharging profiles, and profile endpoints. Thecharging and discharging profiles include the shape of such profiles andpossibly time at a particular state of charge or voltage. The chargingendpoints include the percentage of charge in the batteries at which tostart and stop charging charging the batteries, and the dischargingendpoints include the percentage of charge in the batteries at which tostart and stop discharging the batteries. The profiles and parameterscan be stored in look-up tables, for example. External data 26 caninclude, for example, environmental conditions or factors such astemperature, humidity, airflow around the batteries, time of day oryear, and time since turning on the device or vehicle using thebatteries. Also, active cooling or heating of the batteries can be usedas other control variables with parameters of temperature set points,rates, and gradients in time and space. In the case of portableelectronic devices, external data can also include the following withrespect to such devices: usage of applications, user settings, ascheduled event or alarm, a power consumption pattern, a time of day, orthe location of the device. Similarly in the case of an electricalvehicle, external data can include time of day or time next scheduleduse, typical driving patterns for the vehicle, electricity cost versustime (i.e., for example to avoid peak pricing), predicted weatherconditions, planned travel route, or traffic conditions.

The batteries can include a single physical battery or multiple physicalbatteries that collectively provide power. In the case of multiplephysical batteries, the batteries may have the same or differentconstruction or electrochemistry. The process of injecting signals forcharging and discharging of the batteries seeks to optimize charging anddischarging profiles for a particular battery or a pack of batteries.The pack of batteries can be considered as a single battery where thepack collectively operates together, or the pack of batteries can beconsidered multiple physical batteries driven one-by-one. Examples oftypes of batteries include lithium ion, reflow, lead acid, and others.

FIGS. 2-5 are flow charts of methods for active battery management tooptimize charging and discharging profiles and parameters. These methodscan be implemented in, for example, software modules for execution byprocessor 10.

FIG. 2 is a flow chart of a search space method. The search space methodincludes the following steps: receive control information (includingcosts) 30; construct multidimensional space of all possible controlstates 32; constrain space of potential control spaces 34; determinenormal/baseline sampling distribution 36; determine highest utilitysampling distribution 38; and automated control selection withinconstrained space 40.

FIG. 3 is a flow chart of a signal injection method. The signalinjection method includes the following steps: receive set of potentialsignal injections 42; compute spatial and temporal reaches of signalinjections 44; coordinate signal injections in space and time 46;implement signal injections 48; collect response data 50; and associateresponse data with signal injections 52.

The signal injections are changes in charging and discharging profilesand parameters for battery management. The responses to signal injectionare typically battery performance resulting from or related to thechanges in profiles and parameters from the signal injections. Forexample, the algorithm can perturb values in a look-up tablerepresenting charging and discharging profiles and parameters, and thenmonitor and store the corresponding battery performance response. Thetemporal and spatial reaches of signal injections relate to,respectively, when and where to measure the response signals to thosesignal injections that are used for computing causal relationships. Thecost of signal injection typically relates to how the signal injectionaffects battery performance, for example signal injection can result inlower battery performance, and is controlled by the specifiedexperimental range. The queue for signal injection involves the orderand priority of signal injections and relies on blocking andrandomization to guarantee high internal validity at all times, evenwhen optimizing utility. The utility of responses to signal injectioninvolves the effectiveness of the signal injections or other measures ofutility.

FIG. 4 is a flow chart of a continuous learning method. The continuouslearning method includes the following steps: receive set of potentialsignal injections 54; receive current belief states 56; compute learningvalues for signal injections 58; receive costs for signal injections 60;select and coordinate signal injections 62; implement signal injections64; collect response data 66; and update belief states 68.

The belief states are a set of different models of battery performancein response to charging and discharging. These belief states may haveattached uncertainty values reflecting the likelihood that they areaccurate given the current set of trials and knowledge that may tend toconfirm or falsify these different models, and the information that canfurther confirm or falsify the models may be included in this data orderived from the basic characteristics of the particular model and thephysics of the underlying system.

The learning value is a measure of the value that knowledge generated asa result of the signal injection may provide to subsequentdecision-making by a system, such as determining that a particularcharging or discharging profile is more likely to be optimal.

In the sense of a multi-objective optimization, this can include complextrade-offs between operational goals (e.g., performance versus range)and where optimality may vary over time. The learning value may becomputed through, for example, predicting the raw number of beliefstates that may be falsified according to the predictions of a PartiallyObservable Markov Decision Process (POMDP) or other statistical model,predicted impacts of the signal injection on the uncertainty levels inthe belief states in such models, or experimental power analysescomputing the reduction in uncertainty and narrowing of confidenceintervals based on increasing to the current sample size.

FIG. 5 is a flow chart of a memory management method. The memorymanagement method includes the following steps: receive set ofhistorical clusters 70;

receive set of historical signal injections 72; and compute temporalstability of signal injections for current clusters 74. If the signalinjections from step 74 are stable 76, then the memory management methodexecutes the following steps: receive set of historical external factorstates 78; compute stability of signal injections versus externalfactors states 80; select two states to split cluster across 82 only ifthere is enough variance across the two states and enough data withineach state (after splitting) to be able to drive decisions in each state(i.e., compute confidence intervals); and update set of historicalclusters 84.

A cluster is a group of experimental units that are statisticallyequivalent with respect to the measure causal effects. Within a cluster,effects are measured free of bias and/or confounding effects fromexternal factors, which guarantees that we are measuring causation andnot just correlations/associations. Distribution of measured effectswithin each cluster are approximately normally distributed.

Table 1 provides an algorithm of an embodiment for automaticallygenerating and applying causal knowledge for active battery management.This algorithm can be implemented in software or firmware for executionby processor 10.

TABLE 1 1 inject randomized controlled signals into battery charging anddis- charging based upon changes in charging and discharging profilesand related parameters 2 ensure signal injections occur within normaloperational ranges and constraints 3 monitor battery performance inresponse to the signal injections 4 compute causal knowledge about therelationship between signal in- jections and monitored batteryperformance 5 select optimal signals for the battery performance basedon current causalknowledge and possibly external data

EXAMPLES Method for Maximizing Delivered Power

Power characterizes the amount of energy per unit time. Maximizing poweris a balancing act of minimizing charge time while minimizing the lossin energy capacity with each cycle. In electric vehicle applications,increased power enables faster acceleration and greater performance. Ingrid applications such as peak shaving where a typical charge/dischargecycle is 24 hours, increased power translates to longer life-time and/orsmaller installations.

In this example, we conducted the following experiment: 32 cells wereconnected to a Maccor cycler (see Reference section for details) with 16cells partially aged (N cycles/cell on average) and 16 cells brand new(0 cycle/cell). The search space consisted of a family of spline curvesdefined as cubic Bezier functions (see Reference for definition) thatspecified the charge profile for each cell. For implementation purposes,these charge curves were discretized into 5 constant-current chargesteps. The start point (constant current at 200 mA up to 3.6V) and endpoint (constant current at 100 mA up to 4.2V) were both fixed; anadditional constant voltage step was added at the end of the chargeprofile (V=4.2V, cutoff current=25 mA) to ensure that each cell reachedits full capacity, as commonly done in practice. The 4 independentvariables consisted of the two coordinates (cutoff voltage: V, andconstant current: I) of the two control points of the cubic Bezierfunction. Each independent variable was discretized into 8 levels,resulting in a total of 4096 possible combinations. Once fully charged,the cells were discharged under a fixed discharge profile (constantcurrent at 250 mA to 3V).

We focused on the charge profile primarily for the purpose ofillustration and simplicity, and the disclosed method can be expanded tothe discharge profile as well as any other variable relevant to cellcycling. The Figure Of Merit (FOM) was defined as delivered powercalculated as the discharge energy divided by cycle time (i.e., chargetime+discharge time). Prior to any significant aging of the cells, bothdischarge energy and discharge time were nearly constant across cells(or cycles) and the FOM was driven primarily by charge time. Additionaldependent variables were recorded with each cycle including chargeenergy and charge time. External variables were also recorded with eachcycle to explore their possible effect of the dependent variables andfind clustering opportunities. The external variables included cellidentification (ID), old versus new cell, discharge energy and dischargetime.

Data was recorded over weeks. During the initial explore phase, thealgorithm assigned charge profiles randomly across the search space andbuilt confidence intervals (CIs) around the expected causal effect ofeach IV level on the FOM. Once some of the CIs were significantlydistinct and non-overlapping, which occurred after exploring ˜30% of thetotal search space, the algorithm started exploiting that knowledge byassigning more frequently levels with the highest utility, resulting ina gradual increase in the FOM. The algorithm also started identifyingclusters across which causal effects where statistically different.While the optimum levels may not change across clusters, their relativeeffects did change due to differential ageing and differential initialstate of health.

FIGS. 6A-6D show the search space of all possible charge profiles forthe described experiment. In FIG. 6A, the optimum charge profile isshown as the dashed line with its end points and control handle pointsshown as the dot-dashed line. The chosen FOM (total delivered power) isplotted versus discharge energy. The power values associated with theoptimum charge profile are shown as the dark gray circles. The FOM andthe exploit frequency are also plotted as a function of time. FIG. 6Ashows the search space of all possible charge profiles. FIG. 6B showsthe FOM versus discharge energy. FIG. 6C shows the FOM versus time. FIG.6D shows the exploit frequency.

FIGS. 7A-7C show that, over time, the algorithm identified distincteffect sizes for old cells versus new cells. Once clustering isinitiated, the algorithm exploits charge profiles that are optimum foreach distinct cluster, which may or may not be the same across clusters.FIGS. 7A and 7B illustrate the Example of method for maximizingdelivered power, where E-Cap refers to energy capacity. FIG. 7Cillustrates the Example of method for maximizing delivered power underenvironmental conditions.

Method for Measuring Cell Internal Resistance

Data from the previous experiment was also analyzed to characterize theinternal resistance of the cells during each cycle and over time.Mapping the internal resistance as a function of current and state ofcharge (SOC) can typically be accomplished by performing complete cyclesat various rates. This analysis is time consuming and, furthermore, onlyrepresents the internal resistance at the beginning of the life of thecell.

This analysis can also be inaccurate because the results of once cycleare not independent of the previous cycle. By randomizing with thealgorithm disclosed herein, this effect is mitigated.

Here, data from each actual cycle was used to estimate internalresistance in the following way: for each constant-current step, theinternal resistance value was approximated by R=ΔV/ΔI, where ΔV was thechange in average potential compared to a reference cell cycled at 25 mAand ΔI was the current difference compared to the reference cell. R wasestimated for each cell, each cycle and each of the 5 constant-currentsteps. A nearly continuous curve of internal resistance versus state ofcharge was obtained, illustrating the expected behavior of Li-ionbatteries. These results are shown in FIGS. 8A-8C.

FIG. 8A shows voltage versus capacity for a charge profile assigned bythe algorithm (solid line) versus reference C/20 charge profile (dashedline) illustrating the overpotential AV. FIG. 8B shows SOC andoverpotential versus current. FIG. 8C shows internal resistance versusSOC.

Knowledge of in situ internal resistance over time can be used toimprove the performance and safety of the battery management system byeliminating charge profiles in the search space that could lead tosignificant over- and under-voltage, heating and degradation of thecells. Internal resistance maps can be built from subgroups within thedata, and examples of possible subgroups include but are not limited to:cell age, number of cycles, temperature exposure, cumulative dischargedenergy, average discharge current, average charge current, maximumcharge current, average voltage, manufacturing batch. Changes ininternal resistance can also be used to detect anomalous conditions suchas onset of a short circuit in a battery pack.

Method for Balancing Cells in a Pack

Beyond maximizing power, causal knowledge of the effects of chargeprofile variables on cell power can also be used to optimize otherutility metrics, such as balancing state of health (SOH) or internalresistance across cells. While cell aging tends to be fairly uniform atthe beginning of battery life (within manufacturing tolerances), itbecomes increasingly more heterogeneous and unpredictable with eachadditional charge/discharge cycle. This is an important considerationwhen repackaging used cells into packs for new applications as a way toextent their life time in less demanding applications. One example isreusing EV batteries for grid-level energy storage. The development ofsmart battery management systems that are capable of balancing cellageing is critical to ensure safe, reliable and durable operations andmake the application economically viable. Accurate determination of theinternal resistance of each cell as well as clustering acrosshomogeneous cell groups is an effective mechanism to quantitativelyimplement cell balancing in practice.

Similarly, hybrid vehicles can use systems that combine different typesof cells with different performance and ageing characteristics. Thepower grid can use systems that combine high power and high energystorage. This translates to greater variance in the data, resulting ingreater difficulty to apply standard data analytics techniques forbattery management. The algorithm disclosed herein can address this typeof problem by automatically identifying the minimum set of homogeneousclusters that can be used for reliable causal inference over time. Thealgorithm disclosed herein can also be used to implement experiments onbatteries across different vehicles.

Method for Maximizing Delivered Power Under Different EnvironmentalConditions

In many applications, batteries are exposed to different environmentalconditions due to weather, infrequent operations, and more. For thepurpose of illustration, we focused on ambient temperature as anexternal factor, which is common to many applications and has a largeknown effect of cell performance. We conducted a second set ofexperiments with 8 cells placed in an oven under elevated ambienttemperature of 45° C. The results showed how the optimum charge profilevaries with environmental conditions

References for the Examples

-   Cells: Rechargeable Li-ion polymer battery wound pouch cells,    5.0×30×35 mm, nominal capacity 500 mAh; graphite anode and LiCoO₂    cathode (E-Group, Mt. Laurel, N.J.).-   Maccor: 96 channel, Series 4000 Maccor (Tulsa, Okla.).-   Cubic Bezier function:

Four points P₀, P₁, P₂ and P₃ in the plane or in higher-dimensionalspace define a cubic Bezier curve. The curve starts at P₀ going towardP₁ and arrives at P₃ coming from the direction of P₂. Usually, the curvewill not pass through P₁ or P₂; these points are only there to providedirectional information. The distance between P₁ and P₂ determines “howfar” and “how fast” the curve moves toward P₁ before turning toward P₂.

Writing B_(Pi,Pj,Pk)(t) for the quadratic Bezier curve defined by P_(i),P_(j), and P_(k), the cubic Bezier curve can be defined as an affinecombination of two quadratic Bezier curves:

B(t)=(1−t)B _(P0,P1,P2)(t)+tB _(P1,P2,P3)(t), 0≤t≤1

-   The explicit form of the curve is:

B(t)=(1−t)³ P ₀+3(1−t)² tP ₁+3(1−t)t² P ₂ +t ³ P ₃, 0≤t≤1

-   For some choices of Pi and P2 the curve may intersect itself or    contain a cusp.

1. A method for active battery management, comprising steps of:injecting randomized controlled signals in charging or discharging of abattery; ensuring the signal injections occur within normal operationalranges and constraints; monitoring performance of the battery inresponse to the controlled signals; computing confidence intervals aboutthe causal relationships between the battery performance and thecontrolled signals; and selecting optimal signals for the charging ordischarging of the battery based on the computed confidence intervals.2. The method of claim 1, wherein the controlled signals comprise acharging profile or a discharging profile.
 3. The method of claim 1,wherein the controlled signals comprise a charging rate or a dischargingrate.
 4. The method of claim 1, wherein the controlled signals comprisecharging profile endpoints or discharging profile endpoints.
 5. Themethod of claim 1, wherein the normal operational ranges comprise amultidimensional space of possible control states generated based oncontrol information and operational constraints.
 6. The method of claim1, wherein the selecting step further comprises selecting the optimalsignals based upon external data comprising environmental conditions. 7.The method of claim 6, wherein the environmental conditions comprise atleast one of a temperature, a humidity, or an airflow around thebattery.
 8. The method of claim 1, wherein the selecting step furthercomprises selecting the optimal signals based upon external datacomprising at least one of, with respect to a portable electronicdevice, usage of applications, user settings, a scheduled event oralarm, a power consumption pattern, a time of day, or a location of thedevice.
 9. The method of claim 1, wherein the battery comprises a packof batteries.
 10. A method for active battery management, comprisingsteps of: providing signal injections for charging or discharging of abattery; receiving response signals corresponding with the signalinjections; measuring a utility of the response signals; accessing datarelating to charging or discharging of the battery; and modifying thedata based upon the utility of the response signals.
 11. The method ofclaim 10, wherein the signal injections comprise a charging profile or adischarging profile.
 12. The method of claim 10, wherein the signalinjections comprise a charging rate or a discharging rate.
 13. Themethod of claim 10, wherein the signal injections comprise chargingprofile endpoints or discharging profile endpoints.
 14. The method ofclaim 10, wherein the accessing step comprises accessing a look-uptable.
 15. The method of claim 10, wherein the signal injections have aspatial reach.
 16. The method of claim 10, wherein the signal injectionshave a temporal reach.
 17. The method of claim 10, wherein the modifyingstep further comprises modifying the data based upon external datacomprising environmental conditions.
 18. The method of claim 17, whereinthe environmental conditions comprise at least one of a temperature, ahumidity, or an airflow around the battery.
 19. The method of claim 10,wherein the selecting step further comprises selecting the optimalsignals based upon external data comprising at least one of, withrespect to a portable electronic device, usage of applications, usersettings, a scheduled event or alarm, a power consumption pattern, atime of day, or a location of the device.
 20. The method of claim 10,wherein the battery comprises a pack of batteries.